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Teaching Reading Comprehension by Using Directed Reading Activity (DRA) Method to the First Year Nursing students of Universitas Muhammadiyah Gombong Muhammad As’ad; Hellaisna Nur’aini Garwan
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Bidang Pendidikan, Humaniora dan Agama
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (528.574 KB)

Abstract

This study was about Using Directed Reading Activity (DRA) Method to Teach ReadingComprehension to the First Year Nursing Students of Universitas MuhammadiyahGombong. The problem statements of this research were (1). What is a directed readingactivity method? (2). To what extent is the DRA method able to improve the students’ability in reading comprehension? The objective of this research was to find out whetheror not the DRA method can stimulate the students’ ability in learning readingcomprehension to the first-year Nursing students of Universitas MuhammadiyahGombong. The method applied in this research was an experiment. The sample of thisresearch consisted of 40 students to the first-year students of Universitas Muhammadiyah Gombong. The writers used purposive sampling in this research. They used a reading test that was administered in the pre-test and posttest, and used aquestionnaire as well. The result of this research showed that the first-year students ofUniversitas Muhammadiyah Gombong had an inadequate score in the pre-test. However, after doing the treatment by using the DRA method their reading comprehension got ahigh score in post-test. The data was analyzed by using a t-test and the result shows thatthe t-test value (3, 5), was greater than the t-table value (1,703). It can be concluded thatthe use of the DRA method is very effective to teach reading comprehension.
Penggunaan Large Language Model untuk Dukungan Keputusan Klinis pada Hipertensi dan Diabetes Melitus Tipe 2: Sebuah Scoping Review Rita Rahmawati; Aang Anwarudin; Muhammad As’ad; Aliya Shidqina Salsabila
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 4 No. 4 (2026): Juli : Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informat
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v4i4.1509

Abstract

Hypertension and type 2 diabetes mellitus (T2DM) are two non-communicable diseases with a high burden in primary healthcare and have become important areas of research on clinical decision support using large language models (LLMs) since 2024. Retrieval-Augmented Generation (RAG) is increasingly used to ground LLM outputs in clinical guidelines, yet the specific contribution of embedding models as an independent variable remains underexplored. This scoping review aimed to map the 2024–2026 literature on the use of LLMs to support clinical decision-making for hypertension and T2DM and to identify existing research gaps. The review followed the Arksey and O’Malley framework and PRISMA-ScR reporting guidelines. Searches were conducted in Google Scholar, PubMed/PubMed Central, arXiv, and IEEE Xplore using Boolean combinations of terms related to LLMs, RAG, hypertension, and T2DM. Fifteen studies met the inclusion criteria. The findings indicate that RAG consistently improves answer accuracy and reduces hallucinations compared with prompting-only approaches, although its benefits decrease as the capabilities of the underlying model increase. Only one study directly manipulated the embedding model as an experimental variable and found trade-offs between general and domain-specific embeddings in retrieval sensitivity and specificity. No studies evaluated RAG or embedding models using Indonesian clinical texts. These findings support the potential of RAG for clinical decision support while highlighting the need for further research on embedding models in the Indonesian medical context.